Center for Bioinformatics and Molecular Biostatistics

The problem of relating genotype (as represented by amino acid sequence) to phenotypes is distinguished from standard regression problems by the nature of sequence data. Here we investigate an instance of such a problem where the phenotype of interest is HIV-1 replication capacity and contiguous segments of protease and reverse transcriptase sequence constitutes genotype. A variety of data analytic methods have been proposed in this context. Shortcomings of select techniques are contrasted with the advantages afforded by tree-structured methods. However, tree-structured methods, in turn, have been criticized on grounds of only enjoying modest predictive performance. A number of ensemble approaches (bagging, boosting, random forests) have recently emerged, devised to overcome this deficiency. We evaluate random forests as applied in this setting, and detail why prediction gains obtained in other situations are not realized. Other approaches including logic regression, support vector machines and neural networks are also applied. We interpret results in terms of HIV-1 reverse transcriptase structure and function.